Towards Energy Efficient Scheduling of Manufacturing Systems through Collaboration between Cyber Physical Production and Energy Systems

Autor: Maroua Nouiri, Damien Trentesaux, Abdelghani Bekrar

Publikováno v: Energies, Vol 12, Iss 23, p 4448 (2019)

Currently, enhancing sustainability, and in particular reducing energy consumption, is a huge challenge for manufacturing enterprises. The vision of the fourth industrial revolution (so-called “industry 4.0”) is not only to optimize production an

Externí odkaz: https://doaj.org/article/6f4aae8a9cd844299b1ee62f2f803035

Digital Twin Lifecycle: Core Challenges and Open Issues

Autor: Farah Abdoune, Maroua Nouiri, Olivier Cardin, Pierre Castagna

Publikováno v: Service Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future
Service Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future, 1083, Springer International Publishing, pp.157-167, 2023, Studies in Computational Intelligence, ⟨10.1007/978-3-031-24291-5_13⟩
Service Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future ISBN: 9783031242908

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d625b8f8b36622f3ee1dc5fd0a73eb67
https://hal.science/hal-03980513

Toward Digital twin for sustainable manufacturing: A data-driven approach for energy consumption behavior model generation

Autor: Farah Abdoune, Lorenzo Ragazzini, Maroua Nouiri, Elisa Negri, Olivier Cardin

Publikováno v: Computers in Industry
Computers in Industry, 2023, 150, pp.103949. ⟨10.1016/j.compind.2023.103949⟩

International audience; Traditional manufacturing systems face a major transition challenge toward intelligent and sustainable manufacturing systems. Energy-efficient manufacturing has become one of the main focuses of this transition because of the

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::96f78f2524359c32ff0d6ae1eb836bd0
https://doi.org/10.1016/j.compind.2023.103949

Machine learning for demand forecasting in the physical internet: a case study of agricultural products in Thailand

Autor: Yves Sallez, Maroua Nouiri, Anirut Kantasa-ard, Abdelghani Bekrar, Abdessamad Ait El Cadi

Publikováno v: International Journal of Production Research
International Journal of Production Research, Taylor & Francis, 2020, pp.1-25. ⟨10.1080/00207543.2020.1844332⟩

International audience; Supply chains are complex, stochastic systems. Nowadays, logistics managers face two main problems: increasingly diverse and variable customer demand that is difficult to predict. Classical forecasting methods implemented in m

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4b0ff0115c0e0b798f2bc6fe53a6cab6
https://doi.org/10.1080/00207543.2020.1844332

A Q-Learning Rescheduling Approach to the Flexible Job Shop Problem Combining Energy and Productivity Objectives

Autor: Rami Naimi, Maroua Nouiri, Olivier Cardin

Publikováno v: Sustainability, Vol 13, Iss 13016, p 13016 (2021)
Sustainability; Volume 13; Issue 23; Pages: 13016
Sustainability
Sustainability, MDPI, 2021, ⟨10.3390/su132313016⟩

International audience; The flexible job shop problem (FJSP) has been studied in recent decades due to its dynamic and uncertain nature. Responding to a system's perturbation in an intelligent way and with minimum energy consumption variation is an i

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::684b2af66cc5b937ef518fbd5b23e41b
https://www.mdpi.com/2071-1050/13/23/13016